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https://viurrspace.ca/bitstream/handle/10613/16537/Feb16-1901.pdf?sequence=2

I consider the model of a differentiated duopoly with process R&D when goods are either substitute, complements or independent. I propose a non-cooperative two-stage game with two firms producing differentiated goods. In the first stage, firms decide their technologies and in the second stage, they compete in quantities or prices. I evaluate the social welfare within a framework of Cournot and Bertrand competition models with or without investment in research and development. I prove that the Cournot price can be lower than Bertrand price when the R&D technology is relatively inefficient; thus, Cournot market structure can generate larger consumer's surplus and welfare.

The goal is to prove a surprising lower bound for resource augmented nonclairvoyant algorithms for scheduling jobs with sublinear nondecreasing speed-up curves on multiple processors with the objective of average response time. Edmonds and Pruhs in SODA09 prove that for every $e > 0$, there is an algorithm $alg_{e}$ that is $(1!+!epsilon)$-speed $O({1 over e2})$-competitive. A problem, however, is that this algorithm $alg_{e}$ depends on $e$. The goal is to prove that every fixed deterministic nonclairvoyant algorithm has a suboptimal speed threshold, namely for every (graceful) algorithm $alg$, there is a threshold $1!+!beta_{alg}$ that is $beta_{alg} > 0$ away from being optimal such that the algorithm is $Omega({1 over e beta_{alg}})$ competitive with speed $(1 !+! beta_{alg}) !+! e$ and is $omega(1)$ competitive with speed $1 !+! beta_{alg}$. I have worked very hard on it and have felt that I was close. The proof technique is to use Brouwer's fixed point theorem to break the cycle of needing to know which input will be given before one can know what the algorithm will do and needing to know what the algorithm will do before one can know which input to give. Every thing I have can be found at

https://viurrspace.ca/bitstream/handle/10613/21807/Jan18-1893.pdf?sequence=2

We investigate the power of voting among diverse, randomized software agents. With teams of computer Go agents in mind, we develop a novel theoretical model of two-stage noisy voting that builds on recent work in machine learning. This model allows us to reason about a collection of agents with different biases (determined by the first-stage noise models), which, furthermore, apply randomized algorithms to evaluate alternatives and produce votes (captured by the second-stage noise models). We analytically demonstrate that a uniform team, consisting of multiple instances of any single agent, must make a significant number of mistakes, whereas a diverse team converges to perfection as the number of agents grows. Our experiments, which pit teams of computer Go agents against strong agents, provide evidence for the effectiveness of voting when agents are diverse.

https://viurrspace.ca/bitstream/handle/10613/15057/Oct18-1917.pdf?sequence=2&isAllowed=y

https://viurrspace.ca/bitstream/handle/10613/19025/Dec26-1874.pdf?sequence=2&isAllowed=y

Carbon monoxide total column amounts in the atmosphere have been measured in the High Northern Hemisphere (30degrees - 90degrees N, HNH) between January 2002 and December 2003 using infrared spectrometers of high and moderate resolution and the Sun as a light source. They were compared to ground-level CO mixing ratios and to total column amounts measured from space by the Terra/MOPITT instrument. All these data reveal increased CO abundances in 2002 - 2003 in comparison to the unperturbed 2000 - 2001 period. Maximum anomalies were observed in September 2002 and August 2003. Using a simple two-box model, the corresponding annual CO emission anomalies (referenced to 2000 - 2001 period) have been found equal to 95 Tg in 2002 and 130 Tg in 2003, thus close to those for 1996 and 1998. A good correlation with hot spots detected by a satellite radiometer allows one to assume strong boreal forest fires, occurred mainly in Russia, as a source of the increased CO burdens.